{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "71893a00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Annotation',\n",
       " 'Arrow',\n",
       " 'Artist',\n",
       " 'AutoLocator',\n",
       " 'Axes',\n",
       " 'Button',\n",
       " 'Circle',\n",
       " 'Figure',\n",
       " 'FigureCanvasBase',\n",
       " 'FixedFormatter',\n",
       " 'FixedLocator',\n",
       " 'FormatStrFormatter',\n",
       " 'Formatter',\n",
       " 'FuncFormatter',\n",
       " 'GridSpec',\n",
       " 'IndexLocator',\n",
       " 'Line2D',\n",
       " 'LinearLocator',\n",
       " 'Locator',\n",
       " 'LogFormatter',\n",
       " 'LogFormatterExponent',\n",
       " 'LogFormatterMathtext',\n",
       " 'LogLocator',\n",
       " 'MaxNLocator',\n",
       " 'MouseButton',\n",
       " 'MultipleLocator',\n",
       " 'Normalize',\n",
       " 'NullFormatter',\n",
       " 'NullLocator',\n",
       " 'Number',\n",
       " 'PolarAxes',\n",
       " 'Polygon',\n",
       " 'Rectangle',\n",
       " 'ScalarFormatter',\n",
       " 'Slider',\n",
       " 'Subplot',\n",
       " 'SubplotSpec',\n",
       " 'Text',\n",
       " 'TickHelper',\n",
       " 'Widget',\n",
       " '_INSTALL_FIG_OBSERVER',\n",
       " '_IP_REGISTERED',\n",
       " '_IoffContext',\n",
       " '_IonContext',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__spec__',\n",
       " '_api',\n",
       " '_auto_draw_if_interactive',\n",
       " '_backend_mod',\n",
       " '_copy_docstring_and_deprecators',\n",
       " '_get_backend_mod',\n",
       " '_get_required_interactive_framework',\n",
       " '_interactive_bk',\n",
       " '_log',\n",
       " '_pylab_helpers',\n",
       " '_setup_pyplot_info_docstrings',\n",
       " '_warn_if_gui_out_of_main_thread',\n",
       " '_xkcd',\n",
       " 'acorr',\n",
       " 'angle_spectrum',\n",
       " 'annotate',\n",
       " 'arrow',\n",
       " 'autoscale',\n",
       " 'autumn',\n",
       " 'axes',\n",
       " 'axhline',\n",
       " 'axhspan',\n",
       " 'axis',\n",
       " 'axline',\n",
       " 'axvline',\n",
       " 'axvspan',\n",
       " 'bar',\n",
       " 'bar_label',\n",
       " 'barbs',\n",
       " 'barh',\n",
       " 'bone',\n",
       " 'box',\n",
       " 'boxplot',\n",
       " 'broken_barh',\n",
       " 'cbook',\n",
       " 'cla',\n",
       " 'clabel',\n",
       " 'clf',\n",
       " 'clim',\n",
       " 'close',\n",
       " 'cm',\n",
       " 'cohere',\n",
       " 'colorbar',\n",
       " 'colormaps',\n",
       " 'connect',\n",
       " 'contour',\n",
       " 'contourf',\n",
       " 'cool',\n",
       " 'copper',\n",
       " 'csd',\n",
       " 'cycler',\n",
       " 'delaxes',\n",
       " 'disconnect',\n",
       " 'docstring',\n",
       " 'draw',\n",
       " 'draw_all',\n",
       " 'draw_if_interactive',\n",
       " 'errorbar',\n",
       " 'eventplot',\n",
       " 'figaspect',\n",
       " 'figimage',\n",
       " 'figlegend',\n",
       " 'fignum_exists',\n",
       " 'figtext',\n",
       " 'figure',\n",
       " 'fill',\n",
       " 'fill_between',\n",
       " 'fill_betweenx',\n",
       " 'findobj',\n",
       " 'flag',\n",
       " 'functools',\n",
       " 'gca',\n",
       " 'gcf',\n",
       " 'gci',\n",
       " 'get',\n",
       " 'get_backend',\n",
       " 'get_cmap',\n",
       " 'get_current_fig_manager',\n",
       " 'get_figlabels',\n",
       " 'get_fignums',\n",
       " 'get_plot_commands',\n",
       " 'get_scale_names',\n",
       " 'getp',\n",
       " 'ginput',\n",
       " 'gray',\n",
       " 'grid',\n",
       " 'hexbin',\n",
       " 'hist',\n",
       " 'hist2d',\n",
       " 'hlines',\n",
       " 'hot',\n",
       " 'hsv',\n",
       " 'importlib',\n",
       " 'imread',\n",
       " 'imsave',\n",
       " 'imshow',\n",
       " 'inferno',\n",
       " 'inspect',\n",
       " 'install_repl_displayhook',\n",
       " 'interactive',\n",
       " 'ioff',\n",
       " 'ion',\n",
       " 'isinteractive',\n",
       " 'jet',\n",
       " 'legend',\n",
       " 'locator_params',\n",
       " 'logging',\n",
       " 'loglog',\n",
       " 'magma',\n",
       " 'magnitude_spectrum',\n",
       " 'margins',\n",
       " 'matplotlib',\n",
       " 'matshow',\n",
       " 'minorticks_off',\n",
       " 'minorticks_on',\n",
       " 'mlab',\n",
       " 'new_figure_manager',\n",
       " 'nipy_spectral',\n",
       " 'np',\n",
       " 'pause',\n",
       " 'pcolor',\n",
       " 'pcolormesh',\n",
       " 'phase_spectrum',\n",
       " 'pie',\n",
       " 'pink',\n",
       " 'plasma',\n",
       " 'plot',\n",
       " 'plot_date',\n",
       " 'plotting',\n",
       " 'polar',\n",
       " 'prism',\n",
       " 'psd',\n",
       " 'quiver',\n",
       " 'quiverkey',\n",
       " 'rc',\n",
       " 'rcParams',\n",
       " 'rcParamsDefault',\n",
       " 'rcParamsOrig',\n",
       " 'rc_context',\n",
       " 'rcdefaults',\n",
       " 'rcsetup',\n",
       " 're',\n",
       " 'register_cmap',\n",
       " 'rgrids',\n",
       " 'savefig',\n",
       " 'sca',\n",
       " 'scatter',\n",
       " 'sci',\n",
       " 'semilogx',\n",
       " 'semilogy',\n",
       " 'set_cmap',\n",
       " 'set_loglevel',\n",
       " 'setp',\n",
       " 'show',\n",
       " 'specgram',\n",
       " 'spring',\n",
       " 'spy',\n",
       " 'stackplot',\n",
       " 'stairs',\n",
       " 'stem',\n",
       " 'step',\n",
       " 'streamplot',\n",
       " 'style',\n",
       " 'subplot',\n",
       " 'subplot2grid',\n",
       " 'subplot_mosaic',\n",
       " 'subplot_tool',\n",
       " 'subplots',\n",
       " 'subplots_adjust',\n",
       " 'summer',\n",
       " 'suptitle',\n",
       " 'switch_backend',\n",
       " 'sys',\n",
       " 'table',\n",
       " 'text',\n",
       " 'thetagrids',\n",
       " 'threading',\n",
       " 'tick_params',\n",
       " 'ticklabel_format',\n",
       " 'tight_layout',\n",
       " 'time',\n",
       " 'title',\n",
       " 'tricontour',\n",
       " 'tricontourf',\n",
       " 'tripcolor',\n",
       " 'triplot',\n",
       " 'twinx',\n",
       " 'twiny',\n",
       " 'uninstall_repl_displayhook',\n",
       " 'violinplot',\n",
       " 'viridis',\n",
       " 'vlines',\n",
       " 'waitforbuttonpress',\n",
       " 'winter',\n",
       " 'xcorr',\n",
       " 'xkcd',\n",
       " 'xlabel',\n",
       " 'xlim',\n",
       " 'xscale',\n",
       " 'xticks',\n",
       " 'ylabel',\n",
       " 'ylim',\n",
       " 'yscale',\n",
       " 'yticks']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "dir(plt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "201edff4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12e788a09a0>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1,2,3,4,8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f33befc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12e7813b880>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1,2,3,4,5],[1,2,3,6,10],'g*',label='Green Star')\n",
    "plt.plot([2,5,6,8,9],[5,3,7,9,11],'bD', label=\"Blue Diamond\")\n",
    "plt.title('Scaterplot With Legend')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.legend(loc='best')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a89a8163",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12e7a22af40>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,3)) # 长宽比\n",
    "plt.xlim(0,12) # 坐标轴限制区间\n",
    "plt.ylim(0,12)\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] # 显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False # 坐标轴有负号时可以显示负号\n",
    "plt.plot([1,2,3,4,5],[1,2,3,6,10],'g*',label='绿星')\n",
    "plt.plot([2,5,6,8,9],[5,3,7,9,11],'bD', label=\"蓝钻\")\n",
    "plt.title('带图例的散点图')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.legend(loc='best')\n",
    "# 绘制网格线\n",
    "# plt.grid(color='r', linestyle='--', linewidth=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2de1108b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
